Overview

Dataset statistics

Number of variables17
Number of observations167803
Missing cells0
Missing cells (%)0.0%
Duplicate rows260
Duplicate rows (%)0.2%
Total size in memory23.0 MiB
Average record size in memory144.0 B

Variable types

Numeric14
Categorical3

Alerts

Dataset has 260 (0.2%) duplicate rowsDuplicates
C_WTHR is highly overall correlated with C_RSURHigh correlation
C_RSUR is highly overall correlated with C_WTHRHigh correlation
V_TYPE is highly overall correlated with P_USERHigh correlation
P_PSN is highly overall correlated with P_USERHigh correlation
P_USER is highly overall correlated with V_TYPE and 1 other fieldsHigh correlation
P_USER is highly imbalanced (50.4%)Imbalance
C_HOUR has 2012 (1.2%) zerosZeros

Reproduction

Analysis started2023-03-29 18:04:36.550004
Analysis finished2023-03-29 18:06:43.393804
Duration2 minutes and 6.84 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

C_MNTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6972223
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.422698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4216502
Coefficient of variation (CV)0.51090586
Kurtosis-1.1483379
Mean6.6972223
Median Absolute Deviation (MAD)3
Skewness-0.12319989
Sum1123814
Variance11.70769
MonotonicityIncreasing
2023-03-29T14:06:43.457211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 15878
9.5%
8 15866
9.5%
10 15408
9.2%
6 15203
9.1%
9 14549
8.7%
1 14491
8.6%
11 14207
8.5%
12 14191
8.5%
5 12948
7.7%
2 12539
7.5%
Other values (2) 22523
13.4%
ValueCountFrequency (%)
1 14491
8.6%
2 12539
7.5%
3 11469
6.8%
4 11054
6.6%
5 12948
7.7%
6 15203
9.1%
7 15878
9.5%
8 15866
9.5%
9 14549
8.7%
10 15408
9.2%
ValueCountFrequency (%)
12 14191
8.5%
11 14207
8.5%
10 15408
9.2%
9 14549
8.7%
8 15866
9.5%
7 15878
9.5%
6 15203
9.1%
5 12948
7.7%
4 11054
6.6%
3 11469
6.8%

C_WDAY
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9458949
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.490400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.937695
Coefficient of variation (CV)0.49106605
Kurtosis-1.1794702
Mean3.9458949
Median Absolute Deviation (MAD)2
Skewness0.0037620051
Sum662133
Variance3.7546619
MonotonicityNot monotonic
2023-03-29T14:06:43.519248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 27685
16.5%
4 25386
15.1%
3 24264
14.5%
2 24132
14.4%
1 23375
13.9%
6 22994
13.7%
7 19967
11.9%
ValueCountFrequency (%)
1 23375
13.9%
2 24132
14.4%
3 24264
14.5%
4 25386
15.1%
5 27685
16.5%
6 22994
13.7%
7 19967
11.9%
ValueCountFrequency (%)
7 19967
11.9%
6 22994
13.7%
5 27685
16.5%
4 25386
15.1%
3 24264
14.5%
2 24132
14.4%
1 23375
13.9%

C_HOUR
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.698968
Minimum0
Maximum23
Zeros2012
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.555828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median14
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9486293
Coefficient of variation (CV)0.36124101
Kurtosis-0.12933708
Mean13.698968
Median Absolute Deviation (MAD)3
Skewness-0.44699837
Sum2298728
Variance24.488932
MonotonicityNot monotonic
2023-03-29T14:06:43.592716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16 15550
 
9.3%
17 14886
 
8.9%
15 14266
 
8.5%
14 11871
 
7.1%
12 10944
 
6.5%
18 10836
 
6.5%
13 10774
 
6.4%
8 9013
 
5.4%
11 8813
 
5.3%
19 7801
 
4.6%
Other values (14) 53049
31.6%
ValueCountFrequency (%)
0 2012
 
1.2%
1 1584
 
0.9%
2 1200
 
0.7%
3 1046
 
0.6%
4 871
 
0.5%
5 1639
 
1.0%
6 3947
2.4%
7 6940
4.1%
8 9013
5.4%
9 7366
4.4%
ValueCountFrequency (%)
23 3188
 
1.9%
22 4163
 
2.5%
21 5411
 
3.2%
20 6026
 
3.6%
19 7801
4.6%
18 10836
6.5%
17 14886
8.9%
16 15550
9.3%
15 14266
8.5%
14 11871
7.1%

C_VEHS
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1603845
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.630580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum38
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4799538
Coefficient of variation (CV)0.68504187
Kurtosis249.85121
Mean2.1603845
Median Absolute Deviation (MAD)0
Skewness12.824219
Sum362519
Variance2.1902634
MonotonicityNot monotonic
2023-03-29T14:06:43.668669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 106537
63.5%
1 28986
 
17.3%
3 22982
 
13.7%
4 6132
 
3.7%
5 1545
 
0.9%
6 578
 
0.3%
7 216
 
0.1%
8 143
 
0.1%
10 119
 
0.1%
38 73
 
< 0.1%
Other values (15) 492
 
0.3%
ValueCountFrequency (%)
1 28986
 
17.3%
2 106537
63.5%
3 22982
 
13.7%
4 6132
 
3.7%
5 1545
 
0.9%
6 578
 
0.3%
7 216
 
0.1%
8 143
 
0.1%
9 48
 
< 0.1%
10 119
 
0.1%
ValueCountFrequency (%)
38 73
< 0.1%
33 38
< 0.1%
28 11
 
< 0.1%
27 26
 
< 0.1%
25 44
< 0.1%
24 25
 
< 0.1%
23 25
 
< 0.1%
20 39
< 0.1%
18 23
 
< 0.1%
17 26
 
< 0.1%

C_CONF
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.895079
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.709116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q121
median21
Q335
95-th percentile36
Maximum41
Range40
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.013946
Coefficient of variation (CV)0.46092944
Kurtosis-0.73813967
Mean23.895079
Median Absolute Deviation (MAD)12
Skewness-0.59179004
Sum4009666
Variance121.307
MonotonicityNot monotonic
2023-03-29T14:06:43.746514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
21 56141
33.5%
35 26594
15.8%
36 21505
 
12.8%
6 15195
 
9.1%
33 13154
 
7.8%
22 6514
 
3.9%
31 6093
 
3.6%
4 5286
 
3.2%
2 4508
 
2.7%
3 3672
 
2.2%
Other values (8) 9141
 
5.4%
ValueCountFrequency (%)
1 1121
 
0.7%
2 4508
 
2.7%
3 3672
 
2.2%
4 5286
 
3.2%
5 441
 
0.3%
6 15195
 
9.1%
21 56141
33.5%
22 6514
 
3.9%
23 1919
 
1.1%
24 1372
 
0.8%
ValueCountFrequency (%)
41 1233
 
0.7%
36 21505
12.8%
35 26594
15.8%
34 942
 
0.6%
33 13154
7.8%
32 1617
 
1.0%
31 6093
 
3.6%
25 496
 
0.3%
24 1372
 
0.8%
23 1919
 
1.1%

C_RCFG
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6524973
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.784508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78652683
Coefficient of variation (CV)0.47596256
Kurtosis22.567922
Mean1.6524973
Median Absolute Deviation (MAD)0
Skewness3.2741607
Sum277294
Variance0.61862446
MonotonicityNot monotonic
2023-03-29T14:06:43.815033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 84076
50.1%
1 74596
44.5%
3 6502
 
3.9%
5 1413
 
0.8%
8 409
 
0.2%
4 399
 
0.2%
6 196
 
0.1%
9 156
 
0.1%
10 45
 
< 0.1%
7 11
 
< 0.1%
ValueCountFrequency (%)
1 74596
44.5%
2 84076
50.1%
3 6502
 
3.9%
4 399
 
0.2%
5 1413
 
0.8%
6 196
 
0.1%
7 11
 
< 0.1%
8 409
 
0.2%
9 156
 
0.1%
10 45
 
< 0.1%
ValueCountFrequency (%)
10 45
 
< 0.1%
9 156
 
0.1%
8 409
 
0.2%
7 11
 
< 0.1%
6 196
 
0.1%
5 1413
 
0.8%
4 399
 
0.2%
3 6502
 
3.9%
2 84076
50.1%
1 74596
44.5%

C_WTHR
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5804425
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.844456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1647745
Coefficient of variation (CV)0.7369926
Kurtosis4.5246439
Mean1.5804425
Median Absolute Deviation (MAD)0
Skewness2.1991351
Sum265203
Variance1.3566995
MonotonicityNot monotonic
2023-03-29T14:06:43.874363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 124704
74.3%
3 14682
 
8.7%
2 13381
 
8.0%
4 9926
 
5.9%
6 3381
 
2.0%
5 1201
 
0.7%
7 528
 
0.3%
ValueCountFrequency (%)
1 124704
74.3%
2 13381
 
8.0%
3 14682
 
8.7%
4 9926
 
5.9%
5 1201
 
0.7%
6 3381
 
2.0%
7 528
 
0.3%
ValueCountFrequency (%)
7 528
 
0.3%
6 3381
 
2.0%
5 1201
 
0.7%
4 9926
 
5.9%
3 14682
 
8.7%
2 13381
 
8.0%
1 124704
74.3%

C_RSUR
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5840778
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.907808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1291563
Coefficient of variation (CV)0.71281621
Kurtosis3.9545715
Mean1.5840778
Median Absolute Deviation (MAD)0
Skewness2.1741378
Sum265813
Variance1.274994
MonotonicityNot monotonic
2023-03-29T14:06:43.941145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 117752
70.2%
2 27928
 
16.6%
5 10530
 
6.3%
3 8134
 
4.8%
4 2877
 
1.7%
6 478
 
0.3%
7 67
 
< 0.1%
8 25
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
1 117752
70.2%
2 27928
 
16.6%
3 8134
 
4.8%
4 2877
 
1.7%
5 10530
 
6.3%
6 478
 
0.3%
7 67
 
< 0.1%
8 25
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
9 12
 
< 0.1%
8 25
 
< 0.1%
7 67
 
< 0.1%
6 478
 
0.3%
5 10530
 
6.3%
4 2877
 
1.7%
3 8134
 
4.8%
2 27928
 
16.6%
1 117752
70.2%

C_RALN
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.381048
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:43.975503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9192379
Coefficient of variation (CV)0.66560894
Kurtosis7.9543948
Mean1.381048
Median Absolute Deviation (MAD)0
Skewness2.7917864
Sum231744
Variance0.84499832
MonotonicityNot monotonic
2023-03-29T14:06:44.009358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 135463
80.7%
2 13818
 
8.2%
3 10362
 
6.2%
4 4750
 
2.8%
5 1901
 
1.1%
6 1509
 
0.9%
ValueCountFrequency (%)
1 135463
80.7%
2 13818
 
8.2%
3 10362
 
6.2%
4 4750
 
2.8%
5 1901
 
1.1%
6 1509
 
0.9%
ValueCountFrequency (%)
6 1509
 
0.9%
5 1901
 
1.1%
4 4750
 
2.8%
3 10362
 
6.2%
2 13818
 
8.2%
1 135463
80.7%

C_TRAF
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.129849
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:44.043326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median18
Q318
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.1474628
Coefficient of variation (CV)0.80430252
Kurtosis-1.9566678
Mean10.129849
Median Absolute Deviation (MAD)0
Skewness-0.092600371
Sum1699819
Variance66.38115
MonotonicityNot monotonic
2023-03-29T14:06:44.075919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18 85985
51.2%
1 54410
32.4%
3 21898
 
13.0%
4 2481
 
1.5%
6 855
 
0.5%
2 679
 
0.4%
8 544
 
0.3%
12 318
 
0.2%
11 181
 
0.1%
13 117
 
0.1%
Other values (7) 335
 
0.2%
ValueCountFrequency (%)
1 54410
32.4%
2 679
 
0.4%
3 21898
13.0%
4 2481
 
1.5%
5 58
 
< 0.1%
6 855
 
0.5%
7 38
 
< 0.1%
8 544
 
0.3%
9 14
 
< 0.1%
10 55
 
< 0.1%
ValueCountFrequency (%)
18 85985
51.2%
17 28
 
< 0.1%
16 55
 
< 0.1%
15 87
 
0.1%
13 117
 
0.1%
12 318
 
0.2%
11 181
 
0.1%
10 55
 
< 0.1%
9 14
 
< 0.1%
8 544
 
0.3%

V_TYPE
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7890264
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:44.111954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum23
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6866716
Coefficient of variation (CV)1.5017507
Kurtosis13.705601
Mean1.7890264
Median Absolute Deviation (MAD)0
Skewness3.6963727
Sum300204
Variance7.2182043
MonotonicityNot monotonic
2023-03-29T14:06:44.145039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 152017
90.6%
14 4052
 
2.4%
7 2576
 
1.5%
6 2482
 
1.5%
8 2081
 
1.2%
11 1736
 
1.0%
5 1663
 
1.0%
9 789
 
0.5%
17 198
 
0.1%
23 67
 
< 0.1%
Other values (3) 142
 
0.1%
ValueCountFrequency (%)
1 152017
90.6%
5 1663
 
1.0%
6 2482
 
1.5%
7 2576
 
1.5%
8 2081
 
1.2%
9 789
 
0.5%
10 29
 
< 0.1%
11 1736
 
1.0%
14 4052
 
2.4%
17 198
 
0.1%
ValueCountFrequency (%)
23 67
 
< 0.1%
21 48
 
< 0.1%
18 65
 
< 0.1%
17 198
 
0.1%
14 4052
2.4%
11 1736
1.0%
10 29
 
< 0.1%
9 789
 
0.5%
8 2081
1.2%
7 2576
1.5%

V_YEAR
Real number (ℝ)

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.0704
Minimum1918
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:44.187689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1918
5-th percentile2001
Q12007
median2012
Q32016
95-th percentile2018
Maximum2020
Range102
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8519295
Coefficient of variation (CV)0.0029098582
Kurtosis9.3068141
Mean2011.0704
Median Absolute Deviation (MAD)4
Skewness-1.4219074
Sum3.3746364 × 108
Variance34.245079
MonotonicityNot monotonic
2023-03-29T14:06:44.236197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017 13046
 
7.8%
2018 12632
 
7.5%
2015 11499
 
6.9%
2016 11450
 
6.8%
2014 10953
 
6.5%
2013 10477
 
6.2%
2010 9825
 
5.9%
2012 9607
 
5.7%
2008 9243
 
5.5%
2007 9108
 
5.4%
Other values (60) 59963
35.7%
ValueCountFrequency (%)
1918 1
 
< 0.1%
1920 16
< 0.1%
1927 2
 
< 0.1%
1938 2
 
< 0.1%
1947 3
 
< 0.1%
1950 2
 
< 0.1%
1951 2
 
< 0.1%
1954 1
 
< 0.1%
1955 1
 
< 0.1%
1956 1
 
< 0.1%
ValueCountFrequency (%)
2020 556
 
0.3%
2019 7498
4.5%
2018 12632
7.5%
2017 13046
7.8%
2016 11450
6.8%
2015 11499
6.9%
2014 10953
6.5%
2013 10477
6.2%
2012 9607
5.7%
2011 8326
5.0%

P_SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
91295 
0
76508 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters167803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

Length

2023-03-29T14:06:44.278121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T14:06:44.317257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

Most occurring characters

ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

Most occurring scripts

ValueCountFrequency (%)
Common 167803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 91295
54.4%
0 76508
45.6%

P_AGE
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.325137
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:44.357033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q124
median37
Q354
95-th percentile73
Maximum99
Range98
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.446134
Coefficient of variation (CV)0.49449629
Kurtosis-0.61830736
Mean39.325137
Median Absolute Deviation (MAD)15
Skewness0.29869703
Sum6598876
Variance378.15214
MonotonicityNot monotonic
2023-03-29T14:06:44.403781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 3872
 
2.3%
21 3852
 
2.3%
20 3840
 
2.3%
18 3819
 
2.3%
23 3803
 
2.3%
22 3801
 
2.3%
24 3639
 
2.2%
25 3623
 
2.2%
26 3573
 
2.1%
27 3513
 
2.1%
Other values (89) 130468
77.8%
ValueCountFrequency (%)
1 1936
1.2%
2 753
 
0.4%
3 730
 
0.4%
4 705
 
0.4%
5 743
 
0.4%
6 764
 
0.5%
7 726
 
0.4%
8 742
 
0.4%
9 780
0.5%
10 730
 
0.4%
ValueCountFrequency (%)
99 9
 
< 0.1%
98 14
 
< 0.1%
97 8
 
< 0.1%
96 7
 
< 0.1%
95 13
 
< 0.1%
94 29
 
< 0.1%
93 54
< 0.1%
92 60
< 0.1%
91 77
< 0.1%
90 123
0.1%

P_PSN
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.120427
Minimum11
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-03-29T14:06:44.442413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q313
95-th percentile23
Maximum98
Range87
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.9328005
Coefficient of variation (CV)0.60461451
Kurtosis83.184396
Mean13.120427
Median Absolute Deviation (MAD)0
Skewness8.4253462
Sum2201647
Variance62.929324
MonotonicityNot monotonic
2023-03-29T14:06:44.586992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 123673
73.7%
13 23861
 
14.2%
23 7889
 
4.7%
21 6518
 
3.9%
22 1993
 
1.2%
12 1610
 
1.0%
96 1168
 
0.7%
32 694
 
0.4%
33 199
 
0.1%
31 160
 
0.1%
Other values (2) 38
 
< 0.1%
ValueCountFrequency (%)
11 123673
73.7%
12 1610
 
1.0%
13 23861
 
14.2%
21 6518
 
3.9%
22 1993
 
1.2%
23 7889
 
4.7%
31 160
 
0.1%
32 694
 
0.4%
33 199
 
0.1%
96 1168
 
0.7%
ValueCountFrequency (%)
98 11
 
< 0.1%
97 27
 
< 0.1%
96 1168
 
0.7%
33 199
 
0.1%
32 694
 
0.4%
31 160
 
0.1%
23 7889
 
4.7%
22 1993
 
1.2%
21 6518
 
3.9%
13 23861
14.2%

P_ISEV
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2
90299 
1
76530 
3
 
974

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters167803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

Length

2023-03-29T14:06:44.623544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T14:06:44.664631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 167803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 90299
53.8%
1 76530
45.6%
3 974
 
0.6%

P_USER
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1
120061 
2
43492 
5
 
4052
4
 
198

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters167803
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Length

2023-03-29T14:06:44.698496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T14:06:44.740261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 167803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 120061
71.5%
2 43492
 
25.9%
5 4052
 
2.4%
4 198
 
0.1%

Interactions

2023-03-29T14:06:41.149720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.610457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.809749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.931281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.560906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.706457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.300605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.690397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.147044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.519113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.866182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.433077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.865165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.713584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.220673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.655765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.854894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.014639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.607625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.785384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.370427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.759036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.216984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.585691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.943865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.506947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.955013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.853960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.316344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.728257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.925639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.112140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.681980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.889331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.466884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.851387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.311846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.677988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.045610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.605201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.173605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.019657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.409285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.795270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.993578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.215450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.750143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.989759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.558526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.941823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.402390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.768334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.143125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.700755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.284445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.181080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.508209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.867977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.065789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.328247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.824436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.085117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.656006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.037674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.499473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.864008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.247240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.802172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.401956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.350139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.615084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:21.950187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.149629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.447464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.909453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.200193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.752432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.143050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.604754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.967163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.359742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.916183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.529041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.527343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.710055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.019859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.220760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.552643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.982098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.301139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.846125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.224134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.698728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.063815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.563352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.014505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.641768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.684407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.797036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.086278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.286645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.651037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.050813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.395835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.938339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.308306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.773966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.146304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.655691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.104199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.746418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:39.838010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.885409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.154107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.355780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.750984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.121413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.492208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.027832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.394269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.861512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.222747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.750124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.196349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.852623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:40.096324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:42.079601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.219514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.421999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.849420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.190382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.585669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.115525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.479450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.946330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.304365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.830885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.285260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:37.956292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:40.249770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:42.188526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.298703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.502826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:24.959935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.272279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.692474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.214634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.578591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.044188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.400957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:34.934745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.379704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.073501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:40.415852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:42.285107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.375960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.578853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.067014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.350125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:27.796681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.310486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.673033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.139561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.495105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.035617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.476952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.178145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:40.580087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:42.392875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.465919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.665758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.287179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.439185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.012775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.416600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:30.776171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.245646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.597460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.147339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.586642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.301083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:40.744057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:42.590871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:22.766969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:23.861626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:25.507628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:26.636683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:28.228727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:29.625770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:31.084217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:32.454375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:33.803246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:35.362101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:36.796221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:38.630672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T14:06:41.016658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-29T14:06:44.780168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
C_MNTHC_WDAYC_HOURC_VEHSC_CONFC_RCFGC_WTHRC_RSURC_RALNC_TRAFV_TYPEV_YEARP_AGEP_PSNP_SEXP_ISEVP_USER
C_MNTH1.0000.0140.006-0.0110.0030.009-0.035-0.121-0.007-0.0020.0000.0480.0100.0030.0060.0140.082
C_WDAY0.0141.000-0.081-0.0390.022-0.012-0.026-0.0420.020-0.001-0.033-0.004-0.0340.0840.0000.0030.067
C_HOUR0.006-0.0811.000-0.0500.022-0.0280.0280.0510.009-0.014-0.004-0.005-0.074-0.0240.0890.0580.053
C_VEHS-0.011-0.039-0.0501.000-0.3600.060-0.062-0.088-0.132-0.087-0.0610.1000.0330.0010.0240.0380.014
C_CONF0.0030.0220.022-0.3601.0000.187-0.0070.0410.0190.0120.028-0.0300.003-0.0000.0570.1560.107
C_RCFG0.009-0.012-0.0280.0600.1871.000-0.072-0.073-0.105-0.165-0.0390.0100.0410.0020.0390.0660.024
C_WTHR-0.035-0.0260.028-0.062-0.007-0.0721.0000.6560.0990.0300.001-0.032-0.026-0.0020.0150.0330.034
C_RSUR-0.121-0.0420.051-0.0880.041-0.0730.6561.0000.1000.033-0.018-0.031-0.031-0.0090.0100.0420.066
C_RALN-0.0070.0200.009-0.1320.019-0.1050.0990.1001.0000.1160.066-0.065-0.0350.0160.0200.0710.040
C_TRAF-0.002-0.001-0.014-0.0870.012-0.1650.0300.0330.1161.0000.031-0.025-0.0090.0050.0410.0700.036
V_TYPE0.000-0.033-0.004-0.0610.028-0.0390.001-0.0180.0660.0311.000-0.0170.032-0.0170.1800.1230.819
V_YEAR0.048-0.004-0.0050.100-0.0300.010-0.032-0.031-0.065-0.025-0.0171.0000.0320.0260.0830.0580.093
P_AGE0.010-0.034-0.0740.0330.0030.041-0.026-0.031-0.035-0.0090.0320.0321.000-0.1610.0430.0680.292
P_PSN0.0030.084-0.0240.001-0.0000.002-0.002-0.0090.0160.005-0.0170.026-0.1611.0000.1710.0510.582
P_SEX0.0060.0000.0890.0240.0570.0390.0150.0100.0200.0410.1800.0830.0430.1711.0000.1590.177
P_ISEV0.0140.0030.0580.0380.1560.0660.0330.0420.0710.0700.1230.0580.0680.0510.1591.0000.098
P_USER0.0820.0670.0530.0140.1070.0240.0340.0660.0400.0360.8190.0930.2920.5820.1770.0981.000

Missing values

2023-03-29T14:06:42.770561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-29T14:06:43.077094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

C_MNTHC_WDAYC_HOURC_VEHSC_CONFC_RCFGC_WTHRC_RSURC_RALNC_TRAFV_TYPEV_YEARP_SEXP_AGEP_PSNP_ISEVP_USER
21111232115418120100301121
41116232125318120070271121
5111623212531812007053312
61116232125318120091181121
7112014122112120111381121
10112122112112120071231111
11112122112112120070231312
12112122112112120100491121
13111623322211120061641121
33111314112118120100361121
C_MNTHC_WDAYC_HOURC_VEHSC_CONFC_RCFGC_WTHRC_RSURC_RALNC_TRAFV_TYPEV_YEARP_SEXP_AGEP_PSNP_ISEVP_USER
2722851272123321111120061741121
2722861272123321111120070591111
2722871271423321111120011641111
2722881271423321111120080461121
2722921272323522211120160391111
2722931272323522211120161381222
2722941272323522211120111301121
2722951271723521113120090161121
2722961271723521113120090161322
2722971271723521113120051811111

Duplicate rows

Most frequently occurring

C_MNTHC_WDAYC_HOURC_VEHSC_CONFC_RCFGC_WTHRC_RSURC_RALNC_TRAFV_TYPEV_YEARP_SEXP_AGEP_PSNP_ISEVP_USER# duplicates
1659173221111189201301423129
51182311111189201911432126
1075621336211119200612322126
1869316233211139200901522126
11182311111189201901432125
71182311111189201911732125
20121523521113920141896125
1045621336211119200602222125
1649173221111189201301421125
199101163331321189200801496125